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Martin Lindstrom has spent time with 2,000 families in more than 77 countries to get clues to how they live — resulting in the acquisition of what he likes to call Small Data. In his new book, Small Data: The Tiny Clues That Uncover Huge Trends, he argues that the Small Data explains the why behind what Big Data reveals. Knowledge@Wharton recently spoke with Lindstrom on theKnowledge@Wharton show on Wharton Business Radio on SiriusXM channel 111.

An edited transcript of the conversation follows.

Knowledge@Wharton: I wanted to start with an old line that has been around forever: “Don’t sweat the small stuff.” It seems like the opposite is true, correct?

Martin Lindstrom: Absolutely. The issue right now is that the corporate world has become completely blinded by Big Data. But it’s very, very hard to describe emotions using data. That is where the issue is. A great example of how powerful Small Data is, in fact, the story back to 2002 where the Lego company was almost going bankrupt. What they did was rely on Big Data. They concluded that the instant gratification generation would kill their product. So they changed the size of the small, tiny bricks to huge building blocks. In 2003, the company was almost going into bankruptcy mode.

What happened was that the company decided to go into the homes of consumers across Europe. They met up with this young kid, an 11-year-old German boy, and they asked him, “What are you most proud of?” The kid replied back, “This pair of sneakers.” He showed them an old, worn-down pair of sneakers. Then he said why. He said, “Well, because it shows I’m the best skater in town. If I slide down the skateboard, I am number one, and this is my evidence.” [Inspired that conversation with the boy, and realizing the quality of insights that could be gained by talking with individuals, Lego employed those methods and] … changed the size of the Lego bricks back to the tiny bricks, invented the Lego Movie and today is number one….

Knowledge@Wharton: That’s amazing that something so innocuous as that conversation really changes the path of a major company. We have seen the unbelievable success that they have had over the last decade.

Lindstrom: Absolutely. It’s happening more and more. I think it’s fair to say if you take the top 100 biggest innovations of our time, perhaps around 60% to 65% are really based on Small Data. It’s everything from Snapchat, which was basically discovered by coincidence, to even the Post-It note. The issue here is that as we become so obsessed with Big Data we forget about the creativity. You have to remember that Big Data is all about analyzing the past, but it has nothing to do with the future. Small Data, which I define as seemingly insignificant observations you identify in consumers’ homes, is everything from how you place your shoes to how you hang your paintings. I call those the emotional DNA we leave behind ourselves…. You need the hypothesis first before you start to mine it and find correlations.

“If you take the top 100 biggest innovations of our time, perhaps around 60% to 65% percent are really based on Small Data.”

Knowledge@Wharton: Please explain the difference between Big Data and what we’re talking about here with Small Data.

Lindstrom: Big Data is all about finding correlations in enormous amounts of data. An example would be back in 2012 where Google was analyzing the search algorithms and concluded that they could predict a flu outbreak a couple of days before it would happen based on people typing in the word “flu.”… The whole medical society was now preordering all their pharmaceutical products in advance because they had that warning, which was great. But just recently the Center for Disease Control concluded that Google had been completely wrong. In fact, the numbers were two times of what they should have been because people were not just typing in “flu….”

Big Data is all about finding correlations, but Small Data is all about finding the causation, the reason why. A simple question in a home would actually reveal that these numbers were probably a little bit too optimistic. That is what we forget as we become so obsessed with proving everything with numbers.

Knowledge@Wharton: You share in the book a variety of different examples of this. I wanted to bring up a couple of them because the one that really jumped out first was the fact that you talk about smartphone usage. Let’s be honest: If you don’t have a smartphone, you’re very much in the minority today. The use of a smartphone can collect so much information about people right now.

Lindstrom: It absolutely can. It can tell an enormous amount about who we are and what we’re dreaming about. It can also encapsulate the users of the phone into somewhat of a conclusion about a whole nation, which I find fascinating.

Knowledge@Wharton High School

One of the things I’ve done over the last 10 years is to spend a tremendous amount of time in consumer homes. It’s more than 2,000 homes I either lived in or visited across 77 different countries. You start to get a sense for what’s going on. What is fascinating is that if you take the Russian culture, for example, you will notice that they are not smiling a lot. In fact, they are very introverted. If you take the Saudi Arabian culture, you’ll notice that there is not a lot of water there. There is not a lot of greenery.

Now if I go back to the smartphone and look at the use of emojis, you will notice that the number one emoji used for Russia is a smiley. It’s actually a smiley with the hearts. The number one emoji used for Saudi Arabia happened to be a potted plant. The number one usage in UK is the wink because they have this funny, awkward British humor. A whole population can actually be squeezed into a little signal, a little piece of Small Data, which actually first makes sense when you know the culture, when you spend time in the homes. That is the fine balance we’re talking about….

Knowledge@Wharton: You have worked with quite an array of companies over your career. How are they trying to use this data and reach consumers in a more effective manner? How are they affected by this shift and maybe even a growing focus on Small Data?

Lindstrom: What we started to learn right now is that those companies which are completely reliant on Big Data actually have started to have a problem. The best example is Walmart, which came up with the second profit warning just recently. They had the largest data-mining warehouse in the world, period. It gives you a good sense of where we are.

One of my clients is Lowes Foods, which is a North Carolina-based company. What they have done is to actually live with the consumer. They are living in the community to understand the Small Data, pick them up. As a result, they now have become much more focused on embedding themselves into the community and actually creating a community inside the store.

As you enter their store, they have now created an amazing community where every staff member acts in a character mood, based on Small Data. You have the sausage works where they’re creating handmade sausages in the supermarket, and they even have Halloween sausages glowing in the dark. I’m not kidding. They have a chicken kitchen where they’re dancing in the middle of everything when they have the chicken ready from the oven…. What the customers are telling me when I interview them in the store is, “I feel at home. I feel like my community is coming back.”

This is the essence of what we discovered when we were searching around for Small Data. We learned that the physical community is dying. It’s all moving into a cloud. People start to feel this huge desire for tactile interaction with people, to see people, because the only thing we touch is our smartphone in the morning. So that was a concrete example about how a retailer completely turned around and is now one of the fastest growing in the region because they’re listening to the consumer and the Small Data.

“Big Data is all about finding correlations, but Small Data is all about finding the causation, the reason why.”

Knowledge@Wharton: In some respects, we’ve seen that happen over the last several years, but it still is a process where a lot of the companies don’t buy in 100%. That ends up becoming one of their biggest downfalls….

Lindstrom: Recently I did a speech for 3,000 executives here in New York City. I asked them to raise their hand if they spent at least one or two days in a consumer home over the last year. Two people raised their hands. That shows everything, as I tend to say. If you have a girlfriend or a boyfriend, you wouldn’t describe that person based on, “Well, I love her because she’s 6′ 7″ tall, and I love the four last digits of her cell phone number. They really turn me on.” Right? No, we have to have that emotional aspect.

It’s very tricky for CEOs and senior managers to understand this because they are so reliant on sitting in meetings and meeting rooms behind screens. Suddenly they have to strip that whole identity away from themselves and go into real consumer homes. That’s where I think the younger generation will start to get it.

I’ll tell you one thing: If I were 15 or 18 or 20 or 25 years of age right now, the first thing I would do is to understand deep consumer psychology by spending time in consumer homes … because that is going to be the biggest asset in the future. Every company wants that.

Knowledge@Wharton: If you went back 20 to 30 years, how many CEOs would spend a day or two in the home of a consumer or a couple of consumers? That wouldn’t even be a thought in the process. Now it has to be?

Lindstrom: Now it has to be…. I had the honor of spending time with the founder and the owner of IKEA, Ingvar Kamprad, and I’ll tell you a funny story. I went into one of his stores in Stockholm in Sweden many years ago, and I had to meet up with him. He was nowhere to be seen in the office. So I said to the staff, “Where is he?” They said, “Well, he’s probably at the usual spot.” I said, “Where is that?” [They said] “That is at the check out.” So I went down to the cash registers. Guess what, they were right. He was sitting behind one of the cash registers and checking people out. I said to him, “Why are you doing that?” He said, “Because this is the cheapest and the most efficient research ever. I can ask everyone why they choose it and why they didn’t choose it.” This is the essence of how good business leaders are.

We lost touch with that. Because we’re so busy, we use that as an excuse for not being present. But I think if you take the good upcoming entrepreneurial business leaders right now, like the Gopro founder, [they are] very much hands-on with the real audience. He knows what they’re thinking. He has been in the shoes of a consumer and thinks like them. That instinct can only be established if you’re really present in the homes. If you just look at numbers, you will never establish an instinct like that.

Knowledge@Wharton: How has the Internet affected Small Data?

Lindstrom: Well, it affects us in a very, very smart way. On one hand, you have the large companies like the Amazons and the eBays of the world, which are thriving on Big Data. A lot of small businesses have been made to believe that they have to follow that trend. But I’ll tell you one thing that is really interesting. As you may be aware, Amazon just recently opened their first bricks-and-mortar store in Seattle…. Now why did they do that?

I think the answer very simply is that we think the book sales are flattening out on Amazon and even the Kindle sales are not growing much more anymore. So they’re trying to find other avenues. Now their Big Data is telling them that they have to have a physical interaction…. I recently did the keynote at the American Bookseller Association (ABA) in Denver. As I spoke to these independent booksellers across the world, I said to them, “Aren’t you afraid of Amazon?” They all said exactly the same, “No way, and I’ll tell you why.” They said, “Because they do not embed themselves into the community.”

Because every bookstore today is embedding themselves into the community…. They’re talking with authors, integrating themselves. I met up with a bookstore with ten staff, which is running more than 1,000 book events a year. This is becoming their lives. Now this is also reflective of the power of big and Small Data because remember they are both like a ying and yang where Big Data on the Internet is good at going down the transaction path if you click, pick and run. You could say that the Small Data is fueling the experiential shopping, the feeling of community, the feeling of the senses — all that stuff you can’t replicate online….

What I tend to say is that they are two partners in a dance. We just need to make sure that both are present. We can’t live on one person dancing with himself, right?

“The physical community is dying. It’s all moving into a cloud. People start to feel this huge desire for tactile interaction with people.”

Knowledge@Wharton: How much do you think the use of Small Data will really continue to grow because of the change of generations?

Lindstrom: We’ve gone too far down the track of the Big Data. It’s not just me saying this. We work and interact a lot with Big Data companies. They are almost all of them saying to us, “You’re right. We need that hypothesis to mine our data.” But people don’t want to even listen to it because it’s not fashionable to talk about this.

What we’re seeing happening right now is the pendulum is swinging back again. We will see people say, “Hey, that’s great with those Big Data. But we probably need to find some great hypotheses.” A great example of that is a major bank here in the U.S which just recently [mined] their data, concluding that they had too much churn.

Churn is basically when people are just moving on with the bank accounts and they’re just leaving. They were concluding that people were not happy. So they started to prepare these letters and send them out to all the customers saying, “Why are you not happy?” Just half a day of interviews, with consumers in their homes, revealed that these consumers were not leaving; in fact, they had just gone through a divorce and one of the two has to change their accounts.

That is what I think we’re starting to realize right now. What you have to remember is that as robots and technology take over, we humans will become and will have to become smarter. A good example is the auto-driving Google cars. What they realized was that two or three of those accidents were not because of the car; it was because the humans were overriding the rules set by the computer in the car. Suddenly we had this game going on. And that will continue.

As Big Data tries to become smarter, the human will become even smarter. That’s the reason why we’ll see the future will be all about those people who can add that creativity to that game, who can think differently.

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One Comment So Far

Anumakonda Jagadeesh

Outstanding.
Today Big data has become a buzz word.
The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. But even in the 1950s, decades before anyone uttered the term “big data,” businesses were using basic analytics (essentially numbers in a spreadsheet that were manually examined) to uncover insights and trends.
The new benefits that big data analytics brings to the table, however, are speed and efficiency. Whereas a few years ago a business would have gathered information, run analytics and unearthed information that could be used for future decisions, today that business can identify insights for immediate decisions. The ability to work faster – and stay agile – gives organizations a competitive edge they didn’t have before.However there are reservations from some experts on Big data wider applications.
Here is a debate on Big Vs small data:
Small data is one of the ways that businesses are now drawing back from a kind of obsession with the latest and newest technologies that support more sophisticated business processes. Those promoting small data contend that it’s important for businesses to use their resources efficiently and avoid overspending on certain types of technologies.
Why Small Data?
• Big Data is hard: Doing it at scale and waiting for trickle down benefits can take time. Not to mention the fact that most marketers and online strategists don’t need full-on Big Data to target their campaigns or deliver personalized experiences.
• Small data is all around us: Social channels are rich with small data that is ready to be collected to inform marketing and buyer decisions. At a personal level, we are constantly creating this small data each time we check in, search, browse, post etc., creating a unique signature that provides a glimpse into our digital and physical health
• Small data is at the center of the new CRM : Social CRM used to create a complete picture of customers, their segments, influencers and even competitors, we need to combine insights from social channels and campaigns with Web analytics and transactional data. Small data is the key to building these rich profiles that will be the center of the new CRM solutions.
• ROI: A focus on the last mile of Big Data offers to leverage investments in Small Data ($10 billion and counting according to IDC) spent on upstream systems, tools, and services.
• Data-driven marketing is the next wave: Big (and small) data-driven marketing has the potential to revolutionize the way businesses interact with customers, transform how customers access and consume (and even wear) useful data, and ultimately redefine the relationship between buyers and sellers.
• Consumer examples abound: Consumers have seen the potential of small data to streamline their shopping, power their fitness routine, or deliver recommendations about the best price for their next flight. With more smart, wearable data-driven devices on the way, there promises to be even more market demand for packaged data and data-delivery devices that “fit” the needs of everyday consumers.
• Platform and Tool vendors are starting to pay attention: The promise of operationalizing Big Data and “turning insight into action” is a major tone from many of the big names in tech including SAP, Oracle and EMC.
• It’s about the end-user. Small data is about the end-user, what they need, and how they can take action. Focus on the user first, and a lot of our technology decisions become clearer.
Simple: Small data is the right data, some small data will start life as Big Data, but you shouldn’t need to be a data scientist to understand or apply it for everyday tasks, simple is(Small Data vs. Big Data : Back to the basics, Ahmed Banafa).

Critiques of big data execution
Ulf-Dietrich Reips and Uwe Matzat wrote in 2014 that big data had become a “fad” in scientific research. Researcher Danah Boyd has raised concerns about the use of big data in science neglecting principles such as choosing arepresentative sample by being too concerned about actually handling the huge amounts of data. This approach may lead to results bias in one way or another. Integration across heterogeneous data resources—some that might be considered big data and others not—presents formidable logistical as well as analytical challenges, but many researchers argue that such integrations are likely to represent the most promising new frontiers in science. In the provocative article “Critical Questions for Big Data”, the authors title big data a part of mythology: “large data sets offer a higher form of intelligence and knowledge […], with the aura of truth, objectivity, and accuracy”. Users of big data are often “lost in the sheer volume of numbers”, and “working with Big Data is still subjective, and what it quantifies does not necessarily have a closer claim on objective truth”. Recent developments in BI domain, such as pro-active reporting especially target improvements in usability of big data, through automated filtering of non-useful data and correlations.
Big data analysis is often shallow compared to analysis of smaller data sets. In many big data projects, there is no large data analysis happening, but the challenge is the extract, transform, load part of data preprocessing.
Big data is a buzzword and a “vague term”, but at the same time an “obsession” with entrepreneurs, consultants, scientists and the media. Big data showcases such as Google Flu Trends failed to deliver good predictions in recent years, overstating the flu outbreaks by a factor of two. Similarly, Academy awards and election predictions solely based on Twitter were more often off than on target. Big data often poses the same challenges as small data; and adding more data does not solve problems of bias, but may emphasize other problems. In particular data sources such as Twitter are not representative of the overall population, and results drawn from such sources may then lead to wrong conclusions. Google Translate—which is based on big data statistical analysis of text—does a good job at translating web pages. However, results from specialized domains may be dramatically skewed. On the other hand, big data may also introduce new problems, such as the multiple comparisons problem: simultaneously testing a large set of hypotheses is likely to produce many false results that mistakenly appear significant. Ioannidis argued that “most published research findings are false” due to essentially the same effect: when many scientific teams and researchers each perform many experiments (i.e. process a big amount of scientific data; although not with big data technology), the likelihood of a “significant” result being actually false grows fast – even more so, when only positive results are published. Furthermore, big data analytics results are only as good as the model on which they are predicated. In an example, big data took part in attempting to predict the results of the 2016 U.S. Presidential Election with varying degrees of success. Forbes predicted “If you believe in Big Data analytics, it’s time to begin planning for a Hillary Clinton presidency and all that entails.”.(Wikipedia)
Dr.A.Jagadeesh Nellore(AP),India